{"id":"W4320507175","doi":"10.2196/42324","title":"Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Higher Education, Science, Research and Innovation, Thailand","keywords":"Artificial intelligence; Coronavirus disease 2019 (COVID-19); Support vector machine; Machine learning; Computer science; Feature extraction; Feature (linguistics); Medical imaging; Convolutional neural network; Deep learning; Pattern recognition (psychology); Medicine; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002497597,0.001105951,0.0008015953,0.001978983,0.0002603837,0.0007378634,0.0009567956,0.001025671,0.0005736429],"category_scores_gemma":[0.002771623,0.0002259356,0.0008810712,0.0007178735,0.0002555519,0.0008359641,0.0007475031,0.0005882546,0.0003054526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006599494,"about_ca_system_score_gemma":0.0005827531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003636633,"about_ca_topic_score_gemma":0.002832927,"domain_scores_codex":[0.9990387,0.0002549884,0.00009988727,0.0002254842,0.0002880104,0.00009298147],"domain_scores_gemma":[0.9984415,0.0006478291,0.0001448767,0.0001263216,0.000546396,0.00009305253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001343338,0.001233786,0.113223,0.0004079184,0.0007853506,0.000417706,0.0002030853,0.23665,0.02920116,0.0006904843,0.003087919,0.6127563],"study_design_scores_gemma":[0.00001580384,0.0004426735,0.0108338,0.00002294335,0.00006813122,0.0001703304,0.00006229201,0.9819276,0.005834235,0.0001940763,0.0004102916,0.00001793589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8058386,0.002134089,0.1873928,0.0003964358,0.0001401482,0.0002955522,0.0007111445,0.001477017,0.00161419],"genre_scores_gemma":[0.9136388,0.0004624457,0.08385447,0.0000824587,0.00004525199,0.0001223519,0.001034023,0.00003127124,0.0007289878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003636633,"threshold_uncertainty_score":0.01320875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2043818605481829,"score_gpt":0.4915016004914947,"score_spread":0.2871197399433118,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}